FIG. 1 is a schematic view showing an application scenario of an echo cancellation technology. A far-end signal transmitted from a network passes through a CODEC module, that is, the far-end signal is decoded by the CODEC module, and then is delivered to a DAC (Digital to Analog Converter) for digital/analog conversion, and finally is sent to a loudspeaker for playing. A microphone located in the same place collects a near-end signal, and meanwhile collects the far-end signal (that is, an echo) played by the loudspeaker. After an echo cancellation is performed on the signals collected by the microphone, the echoes in the signals collected by the microphone are canceled, and only the near-end signal is left. The near-end signal is coded by the CODEC module and then is sent to a far end via the network. If the echoes in the signals collected by the microphone are not canceled, after the signals are sent to the far end, the far end hears the sound of its own. Therefore, the objective of the echo cancellation technology is to cancel the echoes as much as possible, and reserve the near-end signal.
An echo cancellation device generally includes two parts: an AEC (Adaptive Echo Canceller), and an RES (Residual Echo Suppressor). In the adaptive echo cancellation, an adaptive filter is used to simulate a spatial echo path, and cancel the echoes in the signals collected by the microphone. Generally, due to the effect of factors such as the noise, the AEC module cannot completely cancel the echoes, so the RES module is required to perform further echo suppression processing on the residual echoes.
The adaptive filtering has such algorithms as: an NLMS (Normalized Least Mean Square) algorithm, an RLS (Recursive Least Square) algorithm, and an MDF (Multi-delay block frequency domain adaptive filter) algorithm (where, the MDF algorithm is an implementation form of the NLMS algorithm in a frequency domain). When the reverberation time is long and a sampling rate is high, the adaptive filter needs a long order. For example, when the reverberation time is 300 ms and the sampling rate is 48 khz, the needed order is 48000×0.3=14400; as a result, the calculation amount of the adaptive filter is very high, thereby increasing the cost of the device.
In the conventional art, to reduce the complexity, the process of sub-band adaptive filtering is adopted to solve this problem. As shown in FIG. 2, the sub-band division is performed on the near-end signal d(n) and the far-end signal x(n) respectively, and a bandwidth of each sub-band is 250 Hz; therefore, when the sampling rate is 8 KHz, 16 sub-bands may be divided in total; when the sampling rate is 16 KHz, 32 sub-bands may be divided in total; when the sampling rate is 32 KHz, 64 sub-bands may be divided in total. Each sub-band uses the NLMS algorithm to perform the echo cancellation, and afterwards, the sub-bands are summarized to obtain a residual echo signal.
In research of the conventional art, the following problems at least exist:
(1) Echo leakage may occur at a sub-band boundary. It is found through debugging that, the echo attenuation of the sub-band adaptive filter is obviously insufficient at the sub-band boundary, and a strong single-frequency signal (which sounds like a bang, affecting the subjective feeling) is usually left.
(2) A convergence effect of the high sub-band adaptive filter is poor.